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@@ -126,6 +126,7 @@ One-shot investigations that don't fit the leaderboard format — claim verifica
 
 | Report | Finding |
 |---|---|
+| [Sovereign TTS head-to-head: 1.7B Apache beats 4B research-license](reports/tts-head-to-head.md) · [chart](reports/tts-head-to-head.png) | Qwen3-TTS-1.7B (Apache) vs Fish-S2-Pro (4B, research license) on one RTX 5090, 150 Seed-TTS-eval EN utterances, both bf16 and neither compiled. Round-trip WER is a tie (0.6% each — Fish's sub-1% claim holds, Qwen matches it); SIM-o 0.699 vs 0.625; but RTFx 2.22× vs 0.39× and first-audio latency 1.72s vs 9.74s. Fish-S2-Pro's serving stack assumes SGLang + torch.compile + datacenter cards (its RTF<0.5 is an H200 number) — out-of-the-box on a consumer GPU the small open model is 5.7× faster at the same intelligibility. Size + serving assumptions, not quality. A compiled-Fish rerun is the obvious follow-up. |
 | [GLM-5.2 autopsy: clever MLA+DSA, still datacenter-only](reports/glm-5-2-autopsy.md) · [chart](reports/glm-5-2-walls.png) | The biggest open-weights drop in months (743B MoE, MIT, 1M context), measured by arithmetic from the published config — not served. Credit first: `glm_moe_dsa` = MLA + DeepSeek sparse attention compresses the KV cache ~57× (1M = ~88 GiB vs ~4.9 TiB). But the weights can't fit 96GB addressable at any quant (743B needs 1.03 bits/weight; smallest ~1.58-bit = ~147 GB), the 1M KV alone ≈ the whole machine, and DSA saves compute not memory. ~235 GB to use 1M context — past a single H200. Clever ≠ consumer; the home-lab move is to wait for a GLM-5.2-Air. |
 | [Qwen3.6-27B pi-tune: the coding tune that works](reports/qwen3-6-27b-pi-tune.md) · [chart](reports/pi-tune-provenance.png) | A community QLoRA SFT of Qwen3.6-27B on REAL non-thinking agent traces, measured controlled vs its base at matched Q6_K across four legs. Quality (93.3 vs 94.0) and synthetic Agentic Score (98.01 vs 98.61) stay flat — but real SWE-bench Verified resolve goes UP 19 → 20/30 (give-ups 8 → 6) and the MTP drafter holds (2.0-2.4× vs base 1.8-2.2×) where Qwopus-Coder's degraded. The first of three Qwen3.6-27B coding tunes to improve real bug-fixing: across all three the synthetic score is a 2.4pt band while real SWE spans 11-20, so training-data provenance (real traces > synthetic distill), not the "agentic coder" label, is what the anchor sees. |
 | [Qwable-3.6-27b: the distill every cheap eval passes, SWE-bench fails](reports/qwable-3.6-27b-q4.md) · [chart](reports/qwable-27b-flat-cliff.png) | A dense Qwen3.6-27B + Fable-5-style SFT, measured controlled vs its base at matched Q4_K_M. Quality (93.4 vs 94.0) AND the synthetic Agentic Score (97.64 vs 98.19) stay flat — but real SWE-bench Verified resolve drops 18 → 11/30 and give-ups rise 7 → 13. Quant ruled out (base Q6 → Q4 = −1 bug). The agentic board would call it neutral; only the reality anchor caught the give-up regression — a 3rd failure mode, and the inverse of the MoE Qwable-v1 (whose synthetic honestly declined). |